Vulnerability identification is crucial to protect the software systems from attacks for cyber security. It is especially important to localize the vulnerable functions among the source code to facilitate the fix. However, it is a challenging and tedious process, and also requires specialized security expertise. Inspired by the work on manually-defined patterns of vulnerabilities from various code representation graphs and the recent advance on graph neural networks, we propose Devign, a general graph neural network based model for graph-level classification through learning on a rich set of code semantic representations. It includes a novel Conv module to efficiently extract useful features in the learned rich node representations for graph-level classification. The model is trained over manually labeled datasets built on 4 diversified large-scale open-source C projects that incorporate high complexity and variety of real source code instead of synthesis code used in previous works. The results of the extensive evaluation on the datasets demonstrate that Devign outperforms the state of the arts significantly with an average of 10.51% higher accuracy and 8.68\% F1 score, increases averagely 4.66% accuracy and 6.37% F1 by the Conv module.
Deep learning (DL) models of code have recently reported great progress for vulnerability detection. In some cases, DL-based models have outperformed static analysis tools. Although many great models have been proposed, we do not yet have a good understanding of these models. This limits the further advancement of model robustness, debugging, and deployment for the vulnerability detection. In this paper, we surveyed and reproduced 9 state-of-the-art (SOTA) deep learning models on 2 widely used vulnerability detection datasets: Devign and MSR. We investigated 6 research questions in three areas…
East China Normal University（华东师范大学）；Guangdong University of Finance & Economics（广东财经大学）
Vulnerability identification is crucial for cyber security in the software-related industry. Early identification methods require significant manual efforts in crafting features or annotating vulnerable code. Although the recent pre-trained models alleviate this issue, they overlook the multiple rich structural information contained in the code itself. In this paper, we propose a novel Multi-View Pre-Trained Model (MV-PTM) that encodes both sequential and multi-type structural information of the source code and uses contrastive learning to enhance code representations. The experiments conducte…
Athena: Vulnerability-Affected Library Identification via Knowledge Graph Completion
Hanoi University of Science and Technology（河内理工大学）；The University of Sydney（悉尼大学）；Singapore Management University（新加坡管理大学）；The University of Melbourne（墨尔本大学）；Phenikaa University（菲尼卡大学）；Singapore University of Technology and Design（新加坡科技设计大学）
Security issues in shipped code can lead to unforeseen device malfunction, system crashes or malicious exploitation by crackers, post-deployment. These vulnerabilities incur a cost of repair and foremost risk the credibility of the company. It is rewarding when these issues are detected and fixed well ahead of time, before release. Common Weakness Estimation (CWE) is a nomenclature describing general vulnerability patterns observed in C code. In this work, we propose a deep learning model that learns to detect some of the common categories of security vulnerabilities in source code efficiently…
We propose a system, named ATVHunter, which can pinpoint the precise vulnerable in-app TPL versions and provide detailed information about the vulnerabilities and TPLs. We propose a two-phase detection approach to identify specific TPL versions. Specifically, we extract the Control Flow Graphs as the coarse-grained feature to match potential TPLs in the pre-defined TPL database, and then extract opcode in each basic block of CFG as the fine-grained feature to identify the exact TPL versions. We build a comprehensive TPL database (189,545 unique TPLs with 3,006,676 versions) as the reference da…
VulDetectBench: Evaluating the Deep Capability of Vulnerability Detection with Large Language Models
Institute of BigData, Fudan University（复旦大学大数据研究院）；School of Computer Science, Fudan University（复旦大学计算机科学学院）；School of Computer Science, Huazhong University of Science and Technology（华中科技大学计算机科学学院）；Purple Mountain Laboratories（紫金山实验室）；School of Software Engineering, Huazhong University of Science and Technology（华中科技大学软件学院）
Security patches in open-source software, providing security fixes to identified vulnerabilities, are crucial in protecting against cyberattacks. Despite the National Vulnerability Database (NVD) publishes identified vulnerabilities, a vast majority of vulnerabilities and their corresponding security patches remain beyond public exposure, e.g., in the open-source libraries that are heavily relied on by developers. An extensive security patches dataset could help end-users such as security companies, e.g., building a security knowledge base, or researchers, e.g., aiding in vulnerability researc…
You Only Train Once: A Flexible Training Framework for Code Vulnerability Detection Driven by Vul-Vector
Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；China University of Mining and Technology, Beijing（中国矿业大学（北京））；Hong Kong University of Science and Technology (Guangzhou), Deep Interdisciplinary Intelligence Lab（香港科技大学（广州）深度跨学科智能实验室）；Hong Kong University of Science and Technology (Guangzhou), Deep Interdisciplinary Intelligence Lab, Institute of Deep Perception Technology, JITRI（香港科技大学（广州）深度跨学科智能实验室，感知技术研究所，JITRI）
Training machine learning approaches for vulnerability identification and producing reliable tools to assist developers in implementing quality software -- free of vulnerabilities -- is challenging due to the lack of large datasets and real data. Researchers have been looking at these issues and building datasets. However, these datasets usually miss natural language artifacts and programming language diversity. We scraped the entire CVE details database for GitHub references and augmented the data with 3 security-related datasets. We used the data to create a ground-truth dataset of natural l…
This paper presents an approach for identification of vulnerable IoT applications. The approach focuses on a category of vulnerabilities that leads to sensitive information leakage which can be identified by using taint flow analysis. Tainted flows vulnerability is very much impacted by the structure of the program and the order of the statements in the code, designing an approach to detect such vulnerability needs to take into consideration such information in order to provide precise results. In this paper, we propose and develop an approach, FlowsMiner, that mines features from the code rel…
With the increasing usage of open-source software (OSS) components, vulnerabilities embedded within them are propagated to a huge number of underlying applications. In practice, the timely application of security patches in downstream software is challenging. The main reason is that such patches do not explicitly indicate their security impacts in the documentation, which would be difficult to recognize for software maintainers and users. However, attackers can still identify these "secret" security patches by analyzing the source code and generate corresponding exploits to compromise not only…
Peer code review has been found to be effective in identifying security vulnerabilities. However, despite practicing mandatory code reviews, many Open Source Software (OSS) projects still encounter a large number of post-release security vulnerabilities, as some security defects escape those. Therefore, a project manager may wonder if there was any weakness or inconsistency during a code review that missed a security vulnerability. Answers to this question may help a manager pinpointing areas of concern and taking measures to improve the effectiveness of his/her project's code reviews in ident…
Free/Open Source Software (FOSS) enables large-scale reuse of preexisting software components. The main drawback is increased complexity in software supply chain management. A common approach to tame such complexity is automated open source compliance, which consists in automating the verication of adherence to various open source management best practices about license obligation fulllment, vulnerability tracking, software composition analysis, and nearby concerns.We consider the problem of auditing a source code base to determine which of its parts have been published before, which is an imp…
